There are many methods of image segmentation in existence. Classical methods of segmenting an image described by a one-dimensional parameter (e.g. luminance) fall into two categories, those based on edge detection (e.g. the Sobel operator) and those based on region detection (e.g. sieves, watershed transforms). Work has also been done on segmentation of images described by two or more parameters (e.g. colour components). For this purpose histogram processing is often used. It has been suggested that a model of gravitational clustering be employed in RGB measurement space, (see Yung et al “Segmentation of colour images based on the gravitational clustering concept” Optical Engineering, Soc of Photo-Optical Instrumentation Engineers 37 No. 3 1 Mar. 1998 pp 989-1000). It has also been suggested to employ gravitational clustering in a measurement space which includes not only colour information, but also location information for each pixel, (see, Hwajeong et al “Colour image segmentation based on clustering using color space distance and neighbourhood relation among pixels” Journal of Korea Inf. Sci. Soc. Software and Applications October 2000, Vol. 27 No. 10 pp 1038-1045).
The prior art methods of segmentation require excessive computational processing before useful results can be achieved. The concept of gravitational clustering depends upon on a relatively large number of iterations per image, with the “mass” of each segment changing as the iterations proceed. Small changes in the initialization and termination conditions have marked and not always predictable effects on performance. Although the use of a multi-dimensional space (incorporating both colour space and pixel location) is beneficial, performance becomes critically dependent on the relative scaling in the respective dimensions. Prior art methods have not been found to work well with sequences of images—video, for example—where rapid and reliable segmentation decisions are essential.